Exploring gender differences in policing: the role of workplace social support on the mental health and wellbeing of parents in policing
Bibliographic record
Abstract
Abstract Police officers are more likely to suffer from mental health conditions compared with other first responders. Women in policing face disproportionately higher risks of anxiety, depression, and sleep disturbances. Workplace social support (WSS) can mitigate these effects, but its interaction with gender and parenthood remains understudied. This study investigates gender differences in the relationship between WSS, mental health, and overall well-being outcomes among police professionals and examines how parenthood moderates these associations. We conducted a secondary analysis of The Job & The Life survey using hierarchical logistic regression to assess anxiety, depression, overall wellbeing, work-life balance and sleep disturbances across WSS levels. Poor WSS was linked to worse outcomes for both genders. Mothers had higher odds of anxiety, depression, and sleep disturbances but reported better work-life balance than fathers. WSS plays a critical role in mitigating adverse outcomes, yet mothers remain vulnerable despite good WSS. This calls for targeted organizational interventions for women and parents.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".